One Stop Shop for Data Professionals.
December 09, 2024

One Stop Shop for Data Professionals.

Austin Franchino | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User

Overall Satisfaction with Databricks Data Intelligence Platform

Databricks is the primary data platform where we land, standardize, clean, transform, and clean our data sources. We utilize the Workflows feature to automate reoccurring tasks and have built internal applications around the reusable workflows. We use the dashboard feature internally to allow customer success teams and business analysts to keep tabs on the performance and outputs of our products. The workloads are orchestrated in Databricks but executed within our own AWS accounts, allowing us to stay compliant with our stringent security requirements.

Pros

  • Thoughtful application of AI assistants during the coding and analysis steps.
  • Intuitive UI for users of varying skill sets.
  • Frequently updated documentation.

Cons

  • Greater support for non spark workloads.
  • Ability to host JAR files on serverless endpoints.
  • Greater democratization to data sources.
  • Migration took a while, as we were largely a Pandas shop.
Very user-friendly, with multiple ways to achieve the same end product. A great degree of flexibility.
Databricks is a true all-in-one platform, and at the time of implementation, it had more features available to us, making it a clear choice over Snowflake. Moving our workloads from local computing to the servers in Databricks gave our start-up staff a great quality of life boost.

Do you think Databricks Data Intelligence Platform delivers good value for the price?

Yes

Are you happy with Databricks Data Intelligence Platform's feature set?

Yes

Did Databricks Data Intelligence Platform live up to sales and marketing promises?

Yes

Did implementation of Databricks Data Intelligence Platform go as expected?

Yes

Would you buy Databricks Data Intelligence Platform again?

Yes

Medium to Large data throughput shops will benefit the most from Databricks Spark processing. Smaller use cases may find the barrier to entry a bit too high for casual use cases. Some of the overhead to kicking off a Spark compute job can actually lead to your workloads taking longer, but past a certain point the performance returns cannot be beat.

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